Information-Geometric Decomposition in Spike Analysis

We present an information-geometric measure to systematically investigate neuronal firing patterns, taking account not only of the second-order but also of higher-order interactions. We begin with the case of two neurons for illustration and show how to test whether or not any pairwise correlation in one period is significantly different from that in the other period. In order to test such a hypothesis of different firing rates, the correlation term needs to be singled out 'orthogonally' to the firing rates, where the null hypothesis might not be of independent firing. This method is also shown to directly associate neural firing with behavior via their mutual information, which is decomposed into two types of information, conveyed by mean firing rate and coincident firing, respectively. Then, we show that these results, using the 'orthogonal' decomposition, are naturally extended to the case of three neurons and n neurons in general.

[1]  Sonja Grün,et al.  Unitary joint events in multiple neuron spiking activity: detection, significance, and interpretation , 1996 .

[2]  M K Habib,et al.  Dynamics of neuronal firing correlation: modulation of "effective connectivity". , 1989, Journal of neurophysiology.

[3]  E. Vaadia,et al.  Spatiotemporal firing patterns in the frontal cortex of behaving monkeys. , 1993, Journal of neurophysiology.

[4]  A. Aertsen,et al.  Spike synchronization and rate modulation differentially involved in motor cortical function. , 1997, Science.

[5]  Shun-ichi Amari,et al.  Methods of information geometry , 2000 .

[6]  Shun-ichi Amari,et al.  Information geometry on hierarchy of probability distributions , 2001, IEEE Trans. Inf. Theory.

[7]  Hiroyuki Ito,et al.  Model Dependence in Quantification of Spike Interdependence by Joint Peri-Stimulus Time Histogram , 2000, Neural Computation.

[8]  A. Aertsen,et al.  Dynamics of neuronal interactions in monkey cortex in relation to behavioural events , 1995, Nature.

[9]  Stefano Panzeri,et al.  A Unified Approach to the Study of Temporal, Correlational, and Rate Coding , 1999, Neural Computation.

[10]  Shun-ichi Amari,et al.  Information-Geometric Measure for Neural Spikes , 2002, Neural Computation.

[11]  Kathryn B. Laskey,et al.  Neural Coding: Higher-Order Temporal Patterns in the Neurostatistics of Cell Assemblies , 2000, Neural Computation.

[12]  B J Richmond,et al.  Excess synchrony in motor cortical neurons provides redundant direction information with that from coarse temporal measures. , 2001, Journal of neurophysiology.